---
title: Overview
description: 'Build powerful AI applications with self-improving memory using Mem0 open-source'
icon: "eye"
iconType: "solid"
---

## Welcome to Mem0 Open Source

Mem0 is a self-improving memory layer for LLM applications that enables personalized AI experiences while saving costs and delighting users. The open-source version gives you complete control over your memory infrastructure.

## Why Choose Mem0 Open Source?

Mem0 open-source provides a powerful, flexible foundation for AI memory management with these key advantages:

1. **Complete Control**: Deploy and manage your own memory infrastructure with full customization capabilities. Perfect for organizations that need data sovereignty and custom integrations.

2. **Flexible Architecture**: Choose from multiple vector databases (Pinecone, Qdrant, Weaviate, Chroma, PGVector), graph stores (Neo4j, Memgraph), and embedding models to fit your specific needs.

3. **Advanced Memory Organization**: Organize memories using `user_id`, `agent_id`, and `run_id` parameters for sophisticated multi-agent, multi-session applications with precise context control.

4. **Rich Integration Ecosystem**: Seamlessly integrate with popular frameworks like LangChain, LlamaIndex, AutoGen, CrewAI, and Vercel AI SDK.

## Core Features

### Memory Management
- **Synchronous & Asynchronous Operations**: Choose between sync and async memory operations based on your application needs
- **Smart Memory Retrieval**: Intelligent search and retrieval with semantic understanding
- **Memory Persistence**: Long-term storage with automatic optimization and cleanup

### Advanced Organization
- **User Context**: Organize memories by user for personalized experiences
- **Agent Isolation**: Separate memories by AI agent for specialized knowledge domains
- **Session Tracking**: Use run IDs to maintain context across different conversation sessions

### Flexible Storage
- **Vector Databases**: Support for Pinecone, Qdrant, Weaviate, Chroma, and PGVector
- **Graph Stores**: Neo4j and Memgraph integration for relationship-based memory
- **Embedding Models**: Multiple embedding providers for optimal performance

## Getting Started

Choose your preferred approach:

- **[Python Quickstart](../python-quickstart)**: Get started with Python SDK
- **[Node.js Quickstart](../node-quickstart)**: Use Mem0 with Node.js/TypeScript
- **[Examples](../examples)**: Explore real-world use cases and implementations

## Next Steps

- Explore [specific features](./async-memory) in detail
- Learn about [graph memory](../graph_memory/overview) capabilities
- Set up [vector databases](../components/vectordbs/overview) and [LLM integrations](../components/llms/overview)
- Check out our [examples](../examples) for practical implementations
- Join our [Discord community](https://mem0.dev/DiD) for support

We're excited to see what you'll build with Mem0 open-source. Let's create smarter, more personalized AI experiences together!
